Key takeaways

Institutions can harness data from machine learning to discover why students withdraw

Machine learning can be applied to early alert systems and take action to support students

Predictive machine learning models are the most useful to improve student retention

Machine learning (ML) can be used to streamline many higher education processes using varying levels of automation. It can be used to provide more accurate information and make intelligent recommendations. In this article, we’ll look at the next level of automation, taking action, and dive into how all three levels of automation—providing accurate information, making recommendations, and taking action—can be used to improve student retention.

Providing insights into student retention

There are a wide variety of factors that contribute to student retention rates. Individual student factors, such as the amount of financial aid received, academic performance, or course load, influence a student’s choice to continue attending or withdraw from an institution. External factors, such as national and local unemployment rates, also play a role. Machine learning models can take all of these factors into account to accurately forecast future retention rates.

We can use ML methodologies to discover the reasons students leave an institution. Machine learning methods can examine tens of thousands of students and dozens of potential factors that contribute to retention and quickly rank and display them. ML lets us harness data in order to provide insight into what internal, economic, or national academic trends may be impacting retention.

Improving general retention through recommendations

Building on retention forecasts and factor insights, ML based systems could make specific recommendations to an institution to improve retention. For example, if the ML model found that students who took summer classes were more likely to be retained, it may prescribe steps to increase summer term enrolment.

Machine learning can be used to recommend courses and schedules that best contribute to student success. In the above example, the models would recommend summer courses that students would succeed in, giving them the confidence to continue their coursework in the fall, and keep them on track to graduation. By maximizing student success, institutions can improve retention rates, ultimately making the campus experience better for students.

Taking action to support students

In perhaps one of its most powerful applications, ML can be used in early warning detection systems that take direct action when a student is at risk of withdrawing. This is particularly beneficial because ML can often spot patterns well in advance and could predict that a student is in need of support before an advisor becomes aware that the student needs help.

When the ML system recognises that a student may be at risk of withdrawing, it could automatically send the student a request to meet with an advisor and help the student schedule that meeting by finding a time that works for both the student and the advisor. As a result of this action, advisors would have an opportunity to intervene and assist students before they make the choice to withdraw. This would help institutions empower all students, not just those who ask for help. This action benefits students by giving them the support they need to succeed and gives institutions the opportunity to reach out to students on a micro level and improve retention one student at a time.

Which machine learning models improve retention?

There are many different kinds of machine learning models, each with their own unique advantages. For working with data that has a time-based component to it, such as in retention forecasting, modern advancements in recurrent neural networks have improved upon previous methods that were far less accurate. Inspired by the neurons in the human brain, these networks allow ML to synthesise large amounts of data to perform a variety of tasks. While neural networks represent much of what is state-of-the-art in ML, they are not always the perfect choice due to their black-box nature, meaning, the inner workings are unclear.

Other methods, such as predictive models based on decision trees that show possible consequences, can offer a lot more insight into their inner workings and provide reasoning for how they arrived at a certain conclusion. These types of methods can be very useful when trying to determine factors that impact retention. Using models that are more transparent can allow the selection process to be explained to an end user and give insight at the institutional level as to the “why” of student retention.

Final thoughts

Machine learning has numerous capabilities that can be applied in the higher education space with varying levels of automation—informing, making recommendations, and taking action. It’s exciting to see how machine learning can bring higher education into the future and benefit students and institutions around the world.